FaVoR：面向相机重定位的体素渲染特征方法
FaVoR: Features via Voxel Rendering for Camera Relocalization
AI总结：
针对现有特征匹配类相机重定位方法对视角和外观变化鲁棒性不足的问题，提出基于稀疏体素地图的FaVoR方法，通过体渲染合成新视角描述子提升匹配效果，室内精度显著优于SOTA，室外性能相当且成本更低。
摘要：
相机重定位方法涵盖从稠密图像对齐到基于查询图像直接回归相机位姿等多种技术路线。其中，稀疏特征匹配因高效、通用且通常轻量化的特性脱颖而出，具备广泛的应用场景。然而，基于特征的方法往往难以应对大幅视角变化与外观变化，易导致匹配失效与位姿估计不准确。\n为克服这一局限，我们提出一种全新方法，采用全局稀疏但局部稠密的2D特征3D表示。通过在帧序列中跟踪并三角化路标点，我们构建了一个稀疏体素地图，该地图经过优化，可渲染跟踪过程中观测到的图像块描述子。给定初始位姿估计后，我们首先利用体渲染技术从体素中合成描述子，再执行特征匹配以估计相机位姿。该方法能够生成未观测视角的描述子，增强了对视角变化的鲁棒性。\n我们在7-Scenes和Cambridge Landmarks数据集上对方法进行了广泛评估。结果表明，在室内环境中，该方法显著优于现有最先进的特征表示技术，中位平移误差最多降低39%；此外，在室外场景下，该方法的性能与其他方法相当，同时内存占用与计算成本更低。
英文摘要：
Camera relocalization methods range from dense image alignment to direct camera pose regression from a query image. Among these, sparse feature matching stands out as an efficient, versatile, and generally lightweight approach with numerous applications. However, feature-based methods often struggle with significant viewpoint and appearance changes, leading to matching failures and inaccurate pose estimates. To overcome this limitation, we propose a novel approach that leverages a globally sparse yet locally dense 3D representation of 2D features. By tracking and triangulating landmarks over a sequence of frames, we construct a sparse voxel map optimized to render image patch descriptors observed during tracking. Given an initial pose estimate, we first synthesize descriptors from the voxels using volumetric rendering and then perform feature matching to estimate the camera pose. This methodology enables the generation of descriptors for unseen views, enhancing robustness to view changes. We extensively evaluate our method on the 7-Scenes and Cambridge Landmarks datasets. Our results show that our method significantly outperforms existing state-of-the-art feature representation techniques in indoor environments, achieving up to a 39% improvement in median translation error. Additionally, our approach yields comparable results to other methods for outdoor scenarios while maintaining lower memory and computational costs.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
